Many applications use methods and systems for position determination, e.g. of a geodetic instrument, a vehicle or the like, which are based on global positioning systems, such as, for example, GPS, GLONASS or the European Galileo system. These Global Navigation Satellite Systems (GNSS) are based on the reception of satellite signals.
The requirements of vehicle guidance or navigation, e.g. in agricultural, mining, trucking or railroad applications, have subtle differences to those of surveying, including a much stronger requirement for continuously available positioning. However, the ability to provide continuously available positioning is impaired when there is poor satellite “visibility” or one or more of the satellites are inoperative, which results in the accuracy of the position determination being degraded. For example, GPS systems require at least four satellites to be “visible” to enable a good position determination.
In many applications Kalman Filters are used to generate an estimate of a trajectory and to allow vehicle guidance in real-time. A Kalman Filter is a recursive estimator that relies on an estimated state from a previous step and current measured data to calculate an estimate for a current state. One example of a system based on a Kalman Filter is Leica Geosystems' Core Algorithm Library (CAL) technology. The CAL system uses GNSS reference station data and rover observation data for the current measured data to calculate an estimate for the current vehicle position. However, this system is also susceptible to degradation due to the aforementioned poor satellite visibility or satellite downtime.
Other position determination and navigation systems use sensor data, e.g. from one or more physical sensors, such as magnetometers, acceleration sensors or angular rate sensors, mounted to the vehicle, in order to derive further information about the vehicle's parameters, such as position, attitude and velocity and/or the variation in such parameters. However, most sensors produce biased data with a dependency on several factors, e.g. time or temperature. Therefore, the biases in the sensor data need to be minimized to yield accurate position determinations.
Furthermore, various sensors are typically located in different positions on the vehicle and therefore the sensors are not located at the vehicle's centre of motion. Furthermore, even though the vehicle is a solid object, different parts of the vehicle accelerate differently, particularly when undergoing angular rotations.
It is well known to combine the GNSS data with the data from one or more vehicle-mounted physical sensors to provide improved position determination systems and methods. However, such solutions remain susceptible to degradation caused by poor satellite visibility.